Many social networks have become very large and widely used and are increasingly leveraged for information, including characteristics of users, such as for placement of advertising for products or services. Social networks uniquely represent relationships and activities among individuals, communities, and organizations and access to social networks is pervasive in many segments of society as well as available on most computing devices. Accordingly, social networks are generally effective platforms for supporting such advertising.
However, methods for clustering, profiling, classifying, and/or modeling social networks, as well as discovering and analyzing topologies represented therein, are often ineffective. For example, current clustering methods for networks do not consider topology discovery or how various topologies describe the nature of communications or message passing among nodes in the networks.
Additionally, current clustering methods consider social networks to be unweighted, thereby assuming equality among each relationship in the network. By considering networks to be unweighted and/or assigning equal weight to each edge connecting network nodes, current methods for clustering social networks ignore the strength of relationships among nodes, which is vital information. Relationship strength information is particularly useful for identifying key influencers, such as for marketing or promotional purposes